Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "69" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 38 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 36 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459853 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 64.61% | -0.122043 | -1.060339 | 0.118799 | 0.206679 | 1.551696 | 1.117934 | -0.022844 | 1.539862 | 0.7530 | 0.7122 | 0.4106 | 2.887215 | 2.608254 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 69.73% | -0.878789 | -1.611093 | 1.021096 | 0.338459 | 0.985204 | 0.865473 | 0.300045 | -0.223748 | 0.8454 | 0.8510 | 0.2290 | 3.972794 | 4.004138 |
| 2459851 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 11.76% | 59.89% | -0.685499 | -1.237935 | 0.749036 | 0.545512 | 0.622107 | 0.998624 | 0.060924 | -0.107096 | 0.7807 | 0.7688 | 0.3153 | 3.765290 | 3.241138 |
| 2459850 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 18.02% | 72.67% | -0.103311 | -1.082340 | 0.147378 | 0.255993 | 1.070015 | 1.427352 | 0.105916 | 1.570887 | 0.7596 | 0.7757 | 0.3392 | 2.688504 | 2.469902 |
| 2459849 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.67% | 43.55% | -0.214203 | -0.952791 | 0.691504 | 0.156716 | 1.974813 | 1.119692 | -0.013531 | 3.375486 | 0.7583 | 0.7686 | 0.3438 | 1.568925 | 1.399421 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 32.16% | 53.77% | -0.025236 | -1.103520 | 1.481235 | -0.739533 | 2.277909 | 2.062161 | -0.085535 | -0.218557 | 0.7375 | 0.7687 | 0.3680 | 2.944524 | 2.736030 |
| 2459847 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.21% | 86.63% | -0.084416 | -1.099331 | 2.084690 | -0.408828 | 0.682791 | 1.667709 | -0.692581 | -0.242520 | 0.7416 | 0.7089 | 0.4135 | 2.897905 | 2.592640 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 50.00% | -0.558906 | -1.230180 | 1.485447 | -0.868910 | -0.084726 | 0.753513 | -0.246226 | 0.541132 | 0.8527 | 0.7114 | 0.4512 | 2.659541 | 2.222598 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.02% | 83.98% | -0.122381 | -0.706538 | 1.591845 | -0.854878 | 2.864249 | 2.412726 | -0.531056 | -0.518960 | 0.7411 | 0.7634 | 0.3669 | 5.956157 | 5.107822 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.547988 | 1.382030 | 0.865875 | 0.428474 | 0.281899 | 8.334031 | 0.756664 | 1.611655 | 0.0318 | 0.0276 | 0.0037 | nan | nan |
| 2459843 | digital_ok | 0.00% | 0.66% | 0.66% | 0.00% | 15.22% | 32.07% | 0.058614 | -0.777224 | -0.534463 | -0.814024 | 0.838233 | 0.831187 | -0.449550 | 0.375456 | 0.7505 | 0.7637 | 0.3760 | 1.628635 | 1.495282 |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -1.077491 | 1.008693 | -0.769090 | 0.557295 | -0.436914 | -0.686589 | 0.691941 | 0.361987 | 0.0277 | 0.0245 | 0.0027 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | -1.022715 | -0.672379 | -1.342021 | 1.812450 | -0.480685 | -1.247211 | 0.680128 | 0.014327 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 61.719302 | 95.250392 | 67.747141 | 72.787672 | 69.497318 | 111.290107 | 619.144088 | 784.028551 | 0.0176 | 0.0165 | 0.0008 | 1.024104 | 1.012470 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0386 | 0.0375 | 0.0021 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.620091 | 0.247225 | 0.172116 | -0.455679 | 1.476151 | 8.505494 | 0.424813 | 3.619893 | 0.0376 | 0.0376 | 0.0025 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.278643 | -0.048724 | -0.984305 | 0.222670 | 3.732599 | 10.778008 | 0.776491 | 1.860149 | 0.0350 | 0.0434 | 0.0013 | nan | nan |
| 2459832 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.806418 | -1.454738 | 0.064950 | -0.089935 | 0.845602 | 1.671166 | -0.219568 | 2.432565 | 0.1032 | 0.0958 | 0.0256 | 1.216262 | 1.203957 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.154322 | 1.335235 | -0.359734 | 1.412400 | 0.649096 | 0.765097 | 1.068327 | 0.130382 | 0.0425 | 0.0436 | 0.0026 | nan | nan |
| 2459830 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.150735 | -1.538530 | 0.065275 | -0.112409 | -0.321759 | 2.288442 | -0.226670 | 2.706243 | 0.1005 | 0.1031 | 0.0300 | 1.255059 | 1.249145 |
| 2459829 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.381977 | -0.556269 | -0.810677 | 0.038499 | 2.349237 | 1.689385 | 1.862574 | 8.103281 | 0.1008 | 0.1017 | 0.0226 | 1.075944 | 1.079068 |
| 2459828 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.061600 | -1.033993 | -0.766283 | -0.000406 | -0.015139 | 3.375789 | 1.264593 | 2.126029 | 0.0900 | 0.0977 | 0.0245 | 1.218223 | 1.211915 |
| 2459827 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.346447 | -0.983178 | -0.096426 | 0.076454 | 1.555559 | 0.827002 | -0.620004 | -0.670635 | 0.1033 | 0.1039 | 0.0246 | 1.231489 | 1.227850 |
| 2459826 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.023112 | -1.247919 | 0.383520 | -0.010189 | -0.429686 | 3.669230 | -0.271155 | 2.820727 | 0.0701 | 0.0606 | 0.0110 | 1.258466 | 1.254717 |
| 2459825 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.031371 | -1.141612 | -0.210937 | 0.228502 | -0.906489 | 1.203532 | -0.844661 | -0.933466 | 0.0979 | 0.1105 | 0.0284 | 1.177871 | 1.173389 |
| 2459824 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.614831 | -1.236384 | -0.177431 | 0.211432 | 0.337583 | 1.959750 | -0.056989 | 0.818988 | 0.1094 | 0.1150 | 0.0276 | 1.223879 | 1.217808 |
| 2459823 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.068947 | -0.683295 | 0.403604 | 0.574588 | -0.711853 | 0.658869 | -0.285360 | -0.307389 | 0.1047 | 0.1086 | 0.0292 | 1.234352 | 1.234605 |
| 2459822 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.543591 | -0.682302 | -0.298436 | -0.005662 | -0.459596 | 1.031031 | -0.142957 | -0.965075 | 0.1007 | 0.1135 | 0.0272 | 1.189636 | 1.187931 |
| 2459821 | digital_ok | 0.00% | 11.29% | 11.29% | 0.00% | 13.16% | 68.42% | 1.884121 | -1.026560 | -0.477945 | -0.264308 | -0.936858 | 0.689744 | -1.388235 | -1.432674 | 0.7383 | 0.6226 | 0.4169 | 3.525956 | 3.002826 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 5.35% | 0.339365 | -0.851591 | -0.481413 | -0.120541 | 2.409847 | 2.856243 | -0.018397 | 3.382165 | 0.7931 | 0.7296 | 0.3825 | 1.786700 | 1.541766 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.915168 | -0.790020 | -0.702017 | -0.315436 | -1.382036 | 0.324285 | -0.515374 | -0.639050 | 0.8382 | 0.7264 | 0.4731 | 2.920820 | 2.452647 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 39.53% | 0.022235 | -0.812629 | 0.166322 | 0.652640 | -0.016882 | 1.245177 | -0.142815 | 0.763016 | 0.8531 | 0.6384 | 0.5548 | 3.082515 | 3.328307 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 81.58% | 0.697577 | -0.366405 | -0.106298 | 0.223352 | -0.333193 | 1.768103 | -0.529502 | 1.722094 | 0.8372 | 0.7351 | 0.4840 | 4.930755 | 4.817696 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.250565 | -0.738536 | -0.870088 | -0.737290 | 2.008405 | 2.616344 | 2.095497 | 7.274280 | 0.8113 | 0.7741 | 0.3663 | 18.435671 | 20.357556 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Temporal Variability | 1.551696 | -1.060339 | -0.122043 | 0.206679 | 0.118799 | 1.117934 | 1.551696 | 1.539862 | -0.022844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Power | 1.021096 | -0.878789 | -1.611093 | 1.021096 | 0.338459 | 0.985204 | 0.865473 | 0.300045 | -0.223748 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 0.998624 | -0.685499 | -1.237935 | 0.749036 | 0.545512 | 0.622107 | 0.998624 | 0.060924 | -0.107096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 1.570887 | -0.103311 | -1.082340 | 0.147378 | 0.255993 | 1.070015 | 1.427352 | 0.105916 | 1.570887 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 3.375486 | -0.214203 | -0.952791 | 0.691504 | 0.156716 | 1.974813 | 1.119692 | -0.013531 | 3.375486 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Temporal Variability | 2.277909 | -1.103520 | -0.025236 | -0.739533 | 1.481235 | 2.062161 | 2.277909 | -0.218557 | -0.085535 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Power | 2.084690 | -1.099331 | -0.084416 | -0.408828 | 2.084690 | 1.667709 | 0.682791 | -0.242520 | -0.692581 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Power | 1.485447 | -0.558906 | -1.230180 | 1.485447 | -0.868910 | -0.084726 | 0.753513 | -0.246226 | 0.541132 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Temporal Variability | 2.864249 | -0.706538 | -0.122381 | -0.854878 | 1.591845 | 2.412726 | 2.864249 | -0.518960 | -0.531056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 8.334031 | 1.547988 | 1.382030 | 0.865875 | 0.428474 | 0.281899 | 8.334031 | 0.756664 | 1.611655 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Temporal Variability | 0.838233 | -0.777224 | 0.058614 | -0.814024 | -0.534463 | 0.831187 | 0.838233 | 0.375456 | -0.449550 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Shape | 1.008693 | -1.077491 | 1.008693 | -0.769090 | 0.557295 | -0.436914 | -0.686589 | 0.691941 | 0.361987 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Power | 1.812450 | -0.672379 | -1.022715 | 1.812450 | -1.342021 | -1.247211 | -0.480685 | 0.014327 | 0.680128 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 784.028551 | 95.250392 | 61.719302 | 72.787672 | 67.747141 | 111.290107 | 69.497318 | 784.028551 | 619.144088 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 8.505494 | 0.247225 | 0.620091 | -0.455679 | 0.172116 | 8.505494 | 1.476151 | 3.619893 | 0.424813 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 10.778008 | -0.048724 | 0.278643 | 0.222670 | -0.984305 | 10.778008 | 3.732599 | 1.860149 | 0.776491 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 2.432565 | 0.806418 | -1.454738 | 0.064950 | -0.089935 | 0.845602 | 1.671166 | -0.219568 | 2.432565 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Power | 1.412400 | -0.154322 | 1.335235 | -0.359734 | 1.412400 | 0.649096 | 0.765097 | 1.068327 | 0.130382 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 2.706243 | 1.150735 | -1.538530 | 0.065275 | -0.112409 | -0.321759 | 2.288442 | -0.226670 | 2.706243 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 8.103281 | -0.556269 | 0.381977 | 0.038499 | -0.810677 | 1.689385 | 2.349237 | 8.103281 | 1.862574 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 3.375789 | -1.033993 | 1.061600 | -0.000406 | -0.766283 | 3.375789 | -0.015139 | 2.126029 | 1.264593 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Temporal Variability | 1.555559 | 0.346447 | -0.983178 | -0.096426 | 0.076454 | 1.555559 | 0.827002 | -0.620004 | -0.670635 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 3.669230 | -1.247919 | 1.023112 | -0.010189 | 0.383520 | 3.669230 | -0.429686 | 2.820727 | -0.271155 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 1.203532 | -1.141612 | 1.031371 | 0.228502 | -0.210937 | 1.203532 | -0.906489 | -0.933466 | -0.844661 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 1.959750 | -0.614831 | -1.236384 | -0.177431 | 0.211432 | 0.337583 | 1.959750 | -0.056989 | 0.818988 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Shape | 1.068947 | -0.683295 | 1.068947 | 0.574588 | 0.403604 | 0.658869 | -0.711853 | -0.307389 | -0.285360 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Shape | 1.543591 | 1.543591 | -0.682302 | -0.298436 | -0.005662 | -0.459596 | 1.031031 | -0.142957 | -0.965075 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Shape | 1.884121 | -1.026560 | 1.884121 | -0.264308 | -0.477945 | 0.689744 | -0.936858 | -1.432674 | -1.388235 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 3.382165 | 0.339365 | -0.851591 | -0.481413 | -0.120541 | 2.409847 | 2.856243 | -0.018397 | 3.382165 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | ee Shape | 0.915168 | 0.915168 | -0.790020 | -0.702017 | -0.315436 | -1.382036 | 0.324285 | -0.515374 | -0.639050 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 1.245177 | -0.812629 | 0.022235 | 0.652640 | 0.166322 | 1.245177 | -0.016882 | 0.763016 | -0.142815 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Variability | 1.768103 | -0.366405 | 0.697577 | 0.223352 | -0.106298 | 1.768103 | -0.333193 | 1.722094 | -0.529502 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | N04 | digital_ok | nn Temporal Discontinuties | 7.274280 | -0.738536 | 1.250565 | -0.737290 | -0.870088 | 2.616344 | 2.008405 | 7.274280 | 2.095497 |